Using Proteins As Markers for Anabolic Steroid Abuse: A New Perspective in Doping Control?
Bibliographic record
Abstract
Drug toxicity is a major concern and has motivated numerous studies to elucidate specific adverse mechanisms, with acetaminophen being the favorite candidate in toxicology studies. Conversely, androgenic anabolic steroids (AASs) also represent a severe public health issue in sports for elite and non-elite athletes. Supraphysiological dosages of AASs are associated with various adverse effects, from cardiovascular to neurological repercussions including liver dysfunction. Yet, few studies have addressed the toxicity of anabolic steroids, and a significant amount of work will be needed to elucidate and understand steroid toxicity properly. This Perspective suggests ideas on how proteomics and liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) can contribute to (1) pinpoint serum proteins affected by substantial doses of anabolic steroids that would represent interesting novel candidates for routine testing and (2) provide additional knowledge on androgenic anabolic steroid toxicity to help raise awareness on the harmful effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".